Though face recognition systems have broad applications, they are vulnerable to attack. Thus, the presentation attack detection (PAD) method is required to improve the security of face recognition systems. To beat the restrictions of previously suggested PAD methods [1]} have proposed a new PAD method that used hybrid features of handcrafted and deep features extracted from the images by the visible-light camera sensor. Their proposed method uses the multilevel local binary pattern (MLBP) to extract skin features from facial images, and CNN to extract deep image features to distinguish between real and offensive facial images, which have stronger discrimination ability than the single image features. They also used the support vector machines (SVM) in order to classify the features of the image to the real and attack classes, the classification error was significantly reduced to \(0.456\%\)  on a CASIA database. [2]} proposed a model combining the fully convolutional network (FCN), with Sparse Representation Classification (SRC) to propose a new approach to partial face recognition, called Dynamic Feature Matching (DFM), to treat partial facial images regardless of size and without needed to align the face. Their proposed DFM method has achieved remarkable accuracy with high efficiency on various partial face databases, including LFW, YTF, and CASIA-NIR-Distance databases in comparison with state-of-the-art Partial Face Recognition (PFR) methods. [3]} have proposed a method based on deep learning, called Deep Dense Face Detector (DDFD), which is able to detect faces in a wide range of orientations using a single model. [4]} have proposed a new method for face detection using deep learning to improve the state-of-the-art faster RCNN framework for generic object detection, by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. Their proposed scheme outperformed some of the state-of-the-art face detection methods compared, making it the best model in terms of ROC curves comparing to the other methods tested on the FDDB benchmark. In addition, [5]} have proposed a method to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. The features are built on top of the feature extraction hierarchy of deep CNN and are summarized from multi-scale mid-level features. By representing a large number of different identities with a small number of hidden variables, highly compact and discriminative features are acquired. And therefore, achieved \(97.45\%\)  face verification accuracy.
